Optical metrology techniques generally referred to as scatterometry offer the potential to characterize parameters of a workpiece during a manufacturing process. In practice, light is directed onto a periodic grating formed in a workpiece and spectra of reflected light is measured and analyzed to characterize the grating parameters. Characterization parameters may include critical dimensions (CD), sidewall angle (SWA), feature height (HT), etc. which affect the polarization and intensity of the light reflected from or transmitted through a material. Characterization of the grating may thereby characterize the workpiece as well as manufacturing process employed in the formation of the grating and the workpiece. For example, optical metrology system 100 depicted in FIG. 1A can be used to determine the profile of a grating 102 formed on a semiconductor wafer 104. Grating 102 can be formed in test areas on wafer 104, such as adjacent to a device formed on wafer 104. The optical metrology system 100 can include a photometric device with a source 106 and a detector 112. Grating 102 is illuminated by an incident beam 108 from source 106. In the present exemplary embodiment, incident beam 108 is directed onto grating 102 at an angle of incidence θi with respect to normal of grating 102 and an azimuth angle φ (i.e., the angle between the plane of incidence beam 108 and the direction of the periodicity of grating 102). Diffracted beam 110 leaves at an angle of θd with respect to normal and is received by detector 112. Detector 112 converts the diffracted beam 110 into a measured metrology signal. To determine the profile of grating 102, optical metrology system 100 includes a processing module 114 configured to receive the measured metrology signal and analyze the measured metrology signal.
Analysis of measured spectra generally involves comparing the measured sample spectra to simulated spectra to deduce a scatterometry model's parameter values that best describe the measured sample. As used herein, “model” refers to a scatterometry model and “parameter” refers to a model parameter of the scatterometry model. FIG. 1B illustrates a method 150 for building parameterized scatterometry model (model) and spectra library beginning with sample spectra (e.g., originating from one or more workpieces). At operation 152, a set of material files are accessed. Material files specify characteristics (e.g., n, k values) of the material(s) from which the measured sample feature is formed. The material files may be defined by a user or received from an upstream processor.
At operation 152, an initial scatterometry model is accessed. A scatterometry user may define an initial model of the expected sample structure by selecting one or more of the material files to assemble a stack of materials corresponding to those present in the periodic grating features to be measured. Alternatively, an initial model may be received as output from an automated source. This initial model may be further parameterized through definition of nominal values of model parameters, such as thicknesses, CD, SWA, HT, edge roughness, corner rounding radius, etc. which characterize the shape of the feature being measured. Depending on whether a 2D model (i.e., a profile) or 3D model is defined, it is not uncommon to have 30-50, or more, such model parameters.
From a parameterized model, simulated spectra for a given set of grating model parameter values may be computed using rigorous diffraction modeling algorithms, such as the Rigorous Coupled Wave Analysis (RCWA) method. Regression analysis is then performed at operation 156 until the parameterized model converges on a set of model parameter values characterizing a final profile model that corresponds to a simulated spectrum which matches the measured diffraction spectra to a predefined matching criterion. The final profile model associated with the matching simulated diffraction signal is presumed to represent the actual profile of the structure from which the model was generated.
The matching simulated spectra and/or associated optimized profile model can then be utilized at operation 157 to generate a set of simulated diffraction spectra by perturbing the values of the parameterized final profile model. The resulting set of simulated diffraction spectra may then be employed by a scatterometry measurement system operating in a production environment to determine whether subsequently measured grating structures have been fabricated according to specifications.
During the regression operation 156, simulated spectra from a set of model parameters for a hypothetical profile are fit to the measured sample spectra. With each regression performed to arrive at the next simulated spectra, a decision on which of the model parameters are to be allowed to float (i.e., to vary) and which are to be fixed is needed. Generally, each model parameter allowed to float will render all other floating model parameters less precise and floating too many model parameters that cannot be precisely determined by the spectra may cause regression algorithms to become unstable. Yet, given that at least some of the model parameters must be allowed to float during the regression analysis, decisions pertaining to which model parameters to float and which to fix are currently made by highly trained engineers knowledgeable on both the subject manufacturing process and on scatterometry. This dependence on expert skill at for proper selection of model parameters to float during the regression may add weeks or even months of engineering time to the method 150. Considerable subjectivity in the apparent capability of scatterometry as a measurement technique is also introduced by a user's parameterization decisions. Considering further the multiplicity of measurement collection points typical in a semiconductor process and the frequency of process changes that may necessitate new models, the need to properly parameterize a scatterometry model poses a significant obstacle to widespread adoption of scatterometry.
An automated method for determining an optimal parameterization of a scatterometry model that can be done more quickly, more rigorously, and without reliance on highly skilled users would therefore be advantageous.